1,320 research outputs found
Application of Audible Signals in Tool Condition Monitoring using Machine Learning Techniques
Machining is always accompanied by many difficulties like tool wear, tool breakage, improper machining conditions, non-uniform workpiece properties and some other irregularities, which are some of major barriers to highly-automated operations. Effective tool condition monitoring (TCM) system provides a best solution to monitor those irregular machining processes and suggest operators to take appropriate actions. Even though a wide variety of monitoring techniques have been developed for the online detection of tool condition, it remains an unsolved problem to look for a reliable, simple and cheap solution. This research work mainly focuses on developing a real-time tool condition monitoring model to detect the tool condition, part quality in machining process by using machine learning techniques through sound monitoring.
The present study shows the development of a process model capable of on-line process monitoring utilizing machine learning techniques to analyze the sound signals collected during machining and train the proposed system to predict the cutting phenomenon during machining. A decision-making system based on the machine learning technique involving Support Vector Machine approach is developed. The developed system is trained with pre-processed data and tested, and the system showed a significant prediction accuracy in different applications which proves to be an effective model in applying to machining process as an on-line process monitoring system. In addition, this system also proves to be effective, cheap, compact and sensory position invariant. The successful development of the proposed TCM system can provide a practical tool to reduce downtime for tool changes and minimize the amount of scrap in metal cutting industry
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Intelligent machining methods for Ti6Al4V: a review
Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems
projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision-
making abilities, and failure detection can be integrated into machining systems with computational
methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process
modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence-
based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are
intrinsically intelligent due to their ability to predict machining response variables. In the context of this
review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining
response (digitization) in the real machining process, often used to validate and (or) introduce data in
the modeling techniques enumerated above. The widespread use of these techniques in the future will
be crucial for the development of the forthcoming machining systems as they provide data about the
machining process, allow its interpretation and quantification in terms of useful information for process
modelling and optimization, which will create machining systems less dependent on direct human
intervention.publishe
In-process tool condition monitoring systems in CNC turning operations
The present study shows the development of in-process tool condition monitoring systems utilizing signal decomposition technique, statistical data analysis, and artificial neural networks system. Two systems; (1) the system based on the multiple regression, (2) the system based on artificial neural networks with back-propagation learning algorithms were developed;The raw signals obtained from two sensors (tri-axial accelerometer and AE sensor) with different machining parameters and tool conditions were examined and decomposed into six components by utilizing a wavelet transformation. The most significant components of each signal were found by statistical method and implemented to develop two in-process tool monitoring systems;Before the multiple regression system was developed, a statistical process was performed to eliminate the effects of machining parameters from the signals of the accelerometer and AE sensor. The prediction performance improved 12.6% from the process;In order to maximize the benefit of artificial neural networks system in tool monitoring systems, a novel approach was performed in this study. A great number of networks structures were tested systemically to find an optimized structure for the artificial networks tool condition monitoring system. The technique provided benefits of not only saving time but also testing all possible structures more accurately compared with the traditional manual trial-and-error methodology;The developed statistical multiple regression tool condition monitoring system showed 90% accuracy, and the developed artificial neural networks tool condition monitoring system showed 97% accuracy from 151 tests with the reject flank wear size of 0.00787 inch (0.2 mm) or larger;The successful development of the tool condition monitoring systems can provide a practical tool to reduce downtime related with tool changes and minimize the amount of scrap in metal cutting industry. Implications of the study and recommendations for further research were provided
Artificial neural networks for vibration based inverse parametric identifications: A review
Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes
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